Mining Protein Contact Maps

The 3D conformation of a protein may be compactly represented in a symmetrical, square, boolean matrix of pairwise, inter-residue contacts, or "contact map". The contact map provides a host of useful information about the protein's structure. In this paper we describe how data mining can be used to extract valuable information from contact maps. For example, clusters of contacts represent certain secondary structures, and also capture non-local interactions, giving clues to the tertiary structure. In this paper we focus on two main tasks: 1) Given the database of protein sequences, discover an extensive set of non-local (frequent) dense patterns in their contact maps, and compile a library of such non-local interactions. 2) Cluster these patterns based on their similarities and evaluate the clustering quality. We show via experiments that our techniques are effective in characterizing contact patterns across different proteins, and can be used to improve contact map prediction for unknown proteins as well as to learn protein folding pathways.

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